Abstract：
:Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define 'correlation feature space' for samples based on the gene expression profiles by iterative employment of Pearson's correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles.
journal_name
Nucleic Acids Resjournal_title
Nucleic acids researchauthors
Ren X,Wang Y,Zhang XS,Jin Qdoi
10.1093/nar/gkt343subject
Has Abstractpub_date
2013-08-01 00:00:00pages
e143issue
14eissn
0305-1048issn
1362-4962pii
gkt343journal_volume
41pub_type
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